The effectiveness of threat detection can be significantly improved, and companies can be kept safer with the help of behavioral risk analytics. The success of this strategy over the coming years will depend on developing solid ML analytics that is drawn from sufficient input data as this technology becomes more widely used in security platforms.
Security leaders are in a constant struggle to maintain their threat detection tools updated to address increasingly sophisticated malware. Unfortunately, the most efficient method for defending businesses against modern malware is no longer provided by traditional threat detection technology that relies on malware signatures.
While signature-based detection, which looks for specific patterns of code that signify malware or the hash of a known bad file in traffic, is excellent for capturing basic malware, it misses emerging threats for which there are no signatures. Additionally, attackers can quickly repackage malware so that it won’t match recognized signatures.
Traditional threat detection also fails to detect insider attacks committed by employees or by an attacker who has access to legitimate credentials as a result of a data breach or phishing scam.
As a result, a lot of businesses are switching to behavioral risk analysis, which employs a different method and needs a tonne of input data to be effective.
Making the Switch to Behavioral Risk Analysis
Behavioral risk analysis looks at network activity for unique and high-risk behavior. Machine learning models that set a baseline for normal network behavior and scan for abnormalities are necessary.
However, not all unusual activities may be risky. Carrying out a risk analysis entails identifying the risk level of actions by obtaining a lot of contextual data, creating a risk score based on that data, evaluating the anomaly in terms of that risk score, and prioritizing it accordingly.
Also Read: How Enterprises Can Strengthen Their Threat Detection and Response
This lowers the number of false positives and reduces the workload of security teams by helping them prioritize. The ability to determine which acts are risky or not depends on the context in which they occur.
Multiple and Complex Factors
Risk estimation is complex and necessitates taking into account numerous variables. Data from various sources must be used as input for behavioral risk assessments. HR and identity data, endpoint management tools, logs from security tools like firewalls, and data from the applications, cloud, and databases are some of these sources.
External data sources like threat feed or employee social media posts are also helpful. Successful behavior analytics solutions require numerous third-party integrations and the capacity to accept a wide variety of data feeds into a data lake or database because of the enormous amount of contextual data necessary.
When done correctly, behavioral risk analysis can increase effectiveness, decrease false positives, and identify zero-day attacks and insider threats that other threat detection techniques cannot. The ML analysis utilized can also yield helpful information about how devices and systems are used, which is a side benefit.
Automated responses to threats can also be made possible via behavioral risk analysis. Several systems can be instantly shut down by modern malware. Human operators can’t react quickly enough to put a stop to this.
If behavioral analytics are appropriately used, alerts can be generated that are accurate enough for automated responses. Since this method provides so much context, automated remediation measures can be exceptionally targeted, such as denying one user access to one system. As a result, unintentional interference with legitimate business operations is less likely.
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